Agentforce Engineering - when standard configuration is not enough
Custom topics, actions and skills for Agentforce – with Apex, LWC, Heroku backend and MCP. We build Agentforce implementations that are not possible with click configuration.
Your advantages with Agentforce Engineering
Engineering, not just configuration
Custom Apex Actions, LWC components, Heroku services, MCP integration – what standard agencies don’t build.
Domain context mechanical engineering
Topics and skills that are familiar with industrial service logic – not generic CRM examples.
From MVP to productive agent
Clear sprint packages with defined outcomes instead of time-and-material time windows.
Salesforce-native and open
End-to-end Salesforce platform, simultaneously MCP- and LLM-agnostic – no vendor lock-in.
Level 2-3: Networking and decision-making
Digitize → Network → Decide → Automate
Each level delivers independent value. You decide where you want to start.
Agentforce implementations work in level 2 (networked service processes) and level 3 (decision intelligence). We build the technical building blocks that make Agentforce reliable on your data – and lay the foundation for Service Decision Intelligence as the next stage.
Why many Agentforce projects get stuck.
Agentforce is powerful as soon as you have to go beyond the click configuration. That’s where most projects end:
- Standard topics are not enough – service logic in mechanical engineering needs custom topics with their own decision logic, not just “extended prompts”.
- Out-of-the-box actions are too generic – complaint triage, warranty check, component lookup or ERP reference require custom Apex Actions, often with external backend.
- LWC components for service UIs are rarely a click task – guided experiences, diagnostic wizards or agent-supported field service cockpits require front-end engineering.
- Data connection beyond Salesforce – IoT telemetry, ERP, knowledge databases, document archives: it doesn’t work without Heroku services, MuleSoft or MCP integrations.
- Prompt engineering without evaluation – many implementations have no measurable response quality. What goes live without an evaluation loop hallucinates in weeks.
- Cross-skill architecture – as soon as several agents and topics interact, platform architecture is required, not a few configured building blocks.
Standard Salesforce agencies deliver admin and config skills. What they do not provide is the engineering depth that Agentforce needs in productive service scenarios.
Our solution:
Agentforce engineering from a single source - from topic to SDI action.
logicline implements Agentforce for Salesforce customers whose requirements go beyond the standard configuration. We combine Salesforce platform expertise (Apex, LWC, Flow, Experience Cloud) with backend engineering (Heroku, Python/Node, MCP server) and data engineering (GRAX, Data Cloud, external data sources) – and bring domain knowledge from over 130 service projects in mechanical engineering.
You get production-ready agents, not demos. With an evaluation loop, observability and architecture that remains sustainable in your Salesforce instance and your tech stack.
If you need AI agents with true domain intelligence – across multiple systems, with proof of source – Service Decision Intelligence (SDI) is the next step.
What we build - engineering capabilities
Custom Topics & Reasoning Chains
Domain-specific topics with clear decision logic: complaint triage, warranty check, maintenance recommendations, spare parts requirements. Including topic routing, escalation logic and hand-off to human agents.
Custom Apex Actions
Own Apex actions for complex operations that standard actions do not cover: ERP calls, multi-level validations, component BOM logic, warranty calculation, credit line check. With clean error handling, logging and reusability.
Heroku backend for computational and AI-intensive logic
Where Salesforce limits end, our Heroku stack begins: Python/Node services for AI inference, vector databases for RAG, MQTT/OPC-UA connection for IoT, long-running calculations, external API orchestration. Own backend, no external SaaS.
LWC components for agent-supported workflows
Guided experiences, diagnostic wizards, co-pilot sidebars, agent-supported field service cockpits. Lightning Web Components with clean state management and connection to Apex and external APIs.
MCP and data integration
Connection of external data sources via the Model Context Protocol – IoT telemetry, GRAX (Salesforce history without API limits), knowledge databases, document archives. Standard protocol, no proprietary glue.
Prompt engineering & evaluation
Structured prompt design, versioning, eval datasets and automatic regression tests. Response quality becomes measurable – and remains measurable, even if the LLM model changes.
What sets us apart from standard salesforce agencies
There is a lot of Salesforce consulting. Engineering depth for Agentforce is rare. What sets us apart:
- Own products prove depth – IOTAM (machine file with its own IoT backend) and SDI (5 skills, MCP-native, LLM-agnostic) are not consulting foils, but productive platforms.
- Full stack – Apex, LWC, Flow, Experience Cloud on the Salesforce side; Python, Node, Heroku, vector DBs, MCP server outside. One team, no hand-off.
- Mechanical engineering industry context – we know service logic, warranty models, component structures, field service reality. Topics and actions are created from domain knowledge, not from standard templates.
- Co-developed integrations – We not only configured Salesforce integrations for TeamViewer (remote support) and Empolis (knowledge management), but also helped develop them. Ecosystem nodes instead of isolated solutions.
- Cost-optimized mixed-shoring – architecture work in Germany, implementation with an experienced team in India. Engineering depth at reasonable daily rates.
The result: productive agents in 6-12 weeks per sprint, depending on the scope.
Concrete offer - engagement models
Discovery Sprint (2 weeks)
When it makes sense: You are evaluating Agentforce and are unsure about the architecture, data connection or use case prioritization.
What you get:
- Architecture blueprint for your specific use case
- Technical feasibility analysis (data connection, custom actions, backend requirements)
- Cost and risk assessment
- Build vs. buy recommendation (e.g. SDI skills instead of in-house development)
Build Sprint (6-8 weeks)
When it makes sense: Use case is defined, you want to have a productive agent or topic set live.
What you get:
- Custom Topics, Actions, LWC components according to Blueprint
- Backend services (if required) on Heroku
- Data connection (Salesforce, ERP, IoT, documents – depending on the use case)
- Eval setup and handover to your team
- Productive agent for 1-2 topics, integrated in Service Cloud / Experience Cloud
Skill extension (ongoing, monthly)
When it makes sense: You have a productive agent and want to expand it step by step – new topics, new data sources, new workflows.
What you get:
- Fixed engineering contingent per month
- Agile expansion according to backlog
- Eval reviews and performance monitoring
Architecture Review (1-2 weeks)
When it makes sense: Existing Agentforce implementation does not perform, hallucinates or does not scale.
What you get:
- Architecture audit (topic design, actions, data connection, prompts, evaluation)
- Concrete hot fix list with cost estimate
- Recommendation for refactoring or extension
3 specific use cases
Service complaint triage
Custom Topic with Apex actions for warranty check and contract lookup. Heroku service for component parts list analysis. LWC component in case layout for triage recommendation with source reference. Processing time halved.
Field Service Copilot
LWC sidebar in the Field Service Mobile Cockpit. Custom actions for machine data, service history, knowledge base lookups. MCP connection to Empolis for technical instructions. Answers directly at the point of use, with citation.
Aftermarket bot in the customer portal
Agentforce agent in Experience Cloud that suggests spare parts, recommends maintenance appointments and recognizes contact events. LWC components for 3D instructions (side effects), Apex actions for contract and order processing.
From Agentforce to Service Decision Intelligence
Agentforce is the front end. If you need AI agents that use data reliably beyond Salesforce – with source verification and LLM agnosticism – Service Decision Intelligence is the next step. SDI delivers the skills that make your Agentforce implementation domain-ready.
Your path: Installed base assessment → Digital machine file → Customer portal & IoT → Service decision intelligence
You don’t have to implement everything at once. Each stage delivers independent value.
Where do you start?
Are you evaluating Agentforce for an initial use case? → Discovery Sprint (2 weeks) delivers blueprint and feasibility.
Use case is clear, you want to become productive? → Build sprint (6-8 weeks) – productive agent including eval and handover.
Do you already have an implementation that is not performing? → Architecture Review (1-2 weeks) – Audit + hot fix list.
Do you need continuous expansion? → Skill extension – fixed engineering contingent.
FAQs
No. You can also use Agentforce without SDI – especially if your data is predominantly stored in Salesforce. SDI becomes relevant where service decisions require data beyond Salesforce boundaries (IoT, ERP, documents) and proof of source is required.
We supplement standard Salesforce consulting with engineering depth. Specifically: where custom Apex, LWC, Heroku backend, data integration or prompt engineering is needed. Often in parallel with the existing agency, not as a replacement.
Fixed price per use case complexity. Discovery sprint with effort estimation provides clarity before a build sprint starts. Mixed shortening reduces the daily rate load.
Three levers: (1) Clean data connection with sources instead of free-form prompts, (2) structured prompt engineering with versioning, (3) eval setup with defined test datasets that also runs in production.
Yes, as long as we build the architecture accordingly. MCP-based integration and cleanly encapsulated prompts are a prerequisite for this. We recommend LLM agnosticism right from the start, especially in regulated service contexts.
Why logicline?
Own products for faster solutions
Digital machine file, Service Decision Intelligence (SDI) and other modules are ready-made software with domain IP – no effort from zero, shorter time-to-value.
Industry depth for machinery manufacturers
Over 130 projects in service and aftermarket. Our team knows the processes before the first configuration begins.
Salesforce Platform, end-to-end
No system discontinuity, no integration project off track. Portals, spare parts stores, IoT, AI agents – all on one platform.
AI decision intelligence, product-ready
SDI combines machine data, CRM context and knowledge base into concrete recommendations for action. Not an experiment – a ready-to-use module.
Your pace, your order
The step model allows you to start where the need is greatest. Each level delivers immediate value and builds on the previous one.
Ready for the next step?
We will show you in 30 minutes what is possible for your company.
